SAPP stands for Static Analysis Post Processor. SAPP takes the raw results of Pysa and Mariana Trench, and makes them explorable both through a command-line interface and a web UI.
SAPP is also available on GitHub Marketplace as a GitHub Action
To run SAPP, you will need Python 3.8 or later. SAPP can be installed through PyPI with pip install fb-sapp.
This guide assumes that you have results from a Pysa run saved in an ~/example directory. If you are new to Pysa, you can follow this tutorial to get started.
The postprocessing will translate the raw output containing models for every analyzed function into a format that is more suitable for exploration.
[~/example]$ sapp --database-name sapp.db analyze taint-output.jsonAfter the results have been processed we can now explore them through the UI and a command-line interface. We will briefly look at both of those methods here.
Start the web interface with
[~/example]$ sapp --database-name sapp.db server --source-directory=<WHERE YOUR CODE LIVES>and visit http://localhost:13337 in your browser (note: the URL displayed in the code output currently will not work). You will be presented with a list of issues that provide access to example traces.
The same information can be accessed through the command-line interface:
[~/example]$ sapp --database-name sapp.db exploreThis will launch a custom IPython interface that is connected to the sqlite file. In this mode, you can dig into the issues that Pyre surfaces. Following is an example of how to use the various commands.
Start by listing all known issues:
==========================================================
Interactive issue exploration. Type 'help' for help.
==========================================================
[ run 1 ]
>>> issues
Issue 1
Code: 5001
Message: Possible shell injection Data from [UserControlled] source(s) may reach [RemoteCodeExecution] sink(s)
Callable: source.convert
Sources: input
Sinks: os.system
Location: source.py:9|22|32
Found 1 issues with run_id 1.
As expected, we have 1 issue. To select it:
[ run 1 ]
>>> issue 1
Set issue to 1.
Issue 1
Code: 5001
Message: Possible shell injection Data from [UserControlled] source(s) may reach [RemoteCodeExecution] sink(s)
Callable: source.convert
Sources: input
Sinks: os.system
Location: source.py:9|22|32
View how the data flows from source to sink:
[ run 1 > issue 1 > source.convert ]
>>> trace
# ⎇ [callable] [port] [location]
1 leaf source source.py:8|17|22
--> 2 source.convert root source.py:9|22|32
3 source.get_image formal(url) source.py:9|22|32
4 leaf sink source.py:5|21|28
Move to the next callable:
[ run 1 > issue 1 > source.convert ]
>>> n
# ⎇ [callable] [port] [location]
1 leaf source source.py:8|17|22
2 source.convert root source.py:9|22|32
--> 3 source.get_image formal(url) source.py:9|22|32
4 leaf sink source.py:5|21|28
Show the source code at that callable:
[ run 1 > issue 1 > source.get_image ]
>>> list
In source.convert [source.py:9|22|32]
4 command = "wget -q https:{}".format(url)
5 return os.system(command)
6
7 def convert() -> None:
8 image_link = input("image link: ")
--> 9 image = get_image(image_link)
^^^^^^^^^^
Move to the next callable and show source code:
[ run 1 > issue 1 > source.get_image ]
>>> n
# ⎇ [callable] [port] [location]
1 leaf source source.py:8|17|22
2 source.convert root source.py:9|22|32
3 source.get_image formal(url) source.py:9|22|32
--> 4 leaf sink source.py:5|21|28
[ run 1 > issue 1 > leaf ]
>>> list
In source.get_image [source.py:5|21|28]
1 import os
2
3 def get_image(url: str) -> int:
4 command = "wget -q https:{}".format(url)
--> 5 return os.system(command)
^^^^^^^
6
7 def convert() -> None:
8 image_link = input("image link: ")
9 image = get_image(image_link)
Jump to the first callable and show source code:
[ run 1 > issue 1 > leaf ]
>>> jump 1
# ⎇ [callable] [port] [location]
--> 1 leaf source source.py:8|17|22
2 source.convert root source.py:9|22|32
3 source.get_image formal(url) source.py:9|22|32
4 leaf sink source.py:5|21|28
[ run 1 > issue 1 > leaf ]
>>> list
In source.convert [source.py:8|17|22]
3 def get_image(url: str) -> int:
4 command = "wget -q https:{}".format(url)
5 return os.system(command)
6
7 def convert() -> None:
--> 8 image_link = input("image link: ")
^^^^^
9 image = get_image(image_link)
You can refer to the help command to get more information about available commands in the command-line interface.
$ sapp --help
Usage: sapp [OPTIONS] COMMAND [ARGS]...
Options:
-v, --verbosity LVL Either CRITICAL, ERROR, WARNING, INFO or
DEBUG
-r, --repository DIRECTORY Root of the repository (regardless of the
directory analyzed)
--database-name, --dbname FILE
--database-engine, --database [sqlite|memory]
database engine to use
--tool [pysa|mariana-trench] tool the data is coming from
-h, --help Show this message and exit.
Commands:
analyze parse static analysis output and save to disk
explore interactive exploration of issues
filter
lint Output DB models in a lint-friendly format
server backend flask server for exploration of issues
update
A single SAPP database can keep track of more than just a single run. This opens up the possibility of reasoning about newly introduced issues in a codebase.
Every invocation of
[~/example]$ sapp --database-name sapp.db analyze taint-output.jsonwill add a single run to the database. An issue can exist over multiple runs (we typically call the issue in a single run an instance). You can select a run from the web UI and look at all the instances of that run. You can also choose to only show the instances of issues that are newly introduced in this run in the filter menu.
Each instance consists of a data flow from a particular source kind (e.g. user-controlled input) into a callable (i.e. a function or method), and a data flow from that callable into a particular sink kind (e.g. RCE).
Note: the data can come from different sources of the same kind and flow into different sinks of the same kind. The traces view of a single instance represents a multitude of traces, not just a single trace.
SAPP filters are used to include/exclude which issues are shown to you by the issue properties you choose. Filters are useful to remove noise from the output from your static analysis tool, so you can focus on the particular properties of issues you care about.
SAPP functionality can be accessed through the web interface or a subcommand of sapp filter.
A filter is required to have a name and at least one other key, excluding description. Filters can be stored as JSON in the following format:
{
"name": "Name of filter",
"description": "Description for the filter",
"features": [
{
"mode": "all of",
"features": ["via:feature1", "feature2"]
},
{
"mode": "any of",
"features": ["always-via:feature3"]
},
{
"mode": "none of",
"features": ["type:feature5"]
}
],
"codes": [5005],
"paths": ["filename.py"],
"callables": ["main.function_name"],
"traceLengthFromSources": [0, 3],
"traceLengthToSinks": [0, 5],
"is_new_issue": false
}You can find some example filters to reference in the pyre-check repo
You can import a filter from a file by running:
[~/example]$ sapp --database-name sapp.db filter import filter-filename.jsonYou can also import all filters within a directory by running:
[~/example]$ sapp --database-name sapp.db filter import path/to/list_of_filtersYou can view a filter in a SAPP DB by running:
[~/example]$ sapp --database-name sapp.db filter export "filter name"
You can export a filter from a SAPP DB to a file by running:
[~/example]$ sapp --database-name sapp.db filter export "filter name" --output-path /path/to/filename.json
You can delete filters by name with:
[~/example]$ sapp --database-name sapp.db filter delete "filter name 1" "filter name 2" "filter name 3"You can apply a filter to a list of issues by run number. For example, the following command will show you a list of issues after applying example-filter to run 1:
[~/example]$ sapp --database-name sapp.db filter issues 1 example-filter.jsonYou can also apply a list of filters to a single list of issues by run number. SAPP will apply each filter individually from the directory you specify to the list of issues and merge results into a single list of issues to show you. For example, the following command will show you a list of issues after applying every filter in list_of_filters to run 1:
[~/example]$ sapp --database-name sapp.db filter issues 1 path/to/list_of_filtersYou can get the output of a filtered run in SARIF by first storing warning codes information from the static analysis tool in SAPP:
sapp --database-name sapp.db update warning-codes taint-metadata.jsonThen running sapp filter issues with --output-format=sarif:
sapp --database-name sapp.db filter issues 1 path/to/list_of_filters --output-format sarifStart by cloning the repo and setting up a virtual environment:
$ git clone [email protected]:facebook/sapp.git && cd sapp
$ python3 -m venv ~/.venvs/sapp
$ source ~/.venvs/sapp/bin/activate
(sapp) $ pip3 install -r requirements.txtRun the flask server in debug mode:
(sapp) $ python3 -m sapp.cli server --debugParse static analysis output and save to disk:
(sapp) $ python3 -m sapp.cli analyze taint-output.jsonInstalling dependencies for frontend:
(sapp) $ cd sapp/ui/frontend && npm installTo run SAPP with hot reloading of the Web UI, you need have the frontend and backend running simultaneously. In a production environment, the frontend application is compiled and served directly by the backend exposed on port 13337. But in a development environment, the frontend runs in port 3000 by default if the PORT environment variable is not set and the backend runs in port 13337. You can indicate to SAPP to run in the development environment with the debug flag.
Run the flask server and react app in development mode:
(sapp) $ python3 -m sapp.cli server --debug
(sapp) $ cd sapp/ui/frontend && npm run-script startThen visit http://localhost:3000 (or http://<HOST>:<PORT> if you have set the HOST and/or PORT environment variable).
SAPP is licensed under the MIT license.
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